Justifying contentious social and political claims using mundane language: An analysis of Canadian right-wing extremism
Bibliographic record
Abstract
There has been a lack of research examining how right-wing extremist groups justify their key claims online to reach a broader audience. This question is even more worrisome when considering a Canadian context, given Canada's state policies on multiculturalism and intolerance of hateful rhetoric. My research draws on the gaps within the literature of right-wing extremism, online spaces, and justification of discourse by conducting a content analysis of 300 Facebook and Twitter posts from the accounts of three Canadian right-wing extremist groups, ID Canada, Soldiers of Odin BC, and Yellow Vests Canada. This article proposes the use of French theorist Boltanski and Thévenot's sociology of critical capacity common worlds to help explain how right-wing extremist groups make arguments that are quite extreme to a broad audience of people on social media. Such claims include advocating for a homogenized Canadian identity and Canadian values, promoting a belief in social decay, and supporting authoritarianism. However, these claims are not overt; rather right-wing extremist groups discuss apolitical topics to mask controversial views.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.027 | 0.018 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".